Contrastive learning is a self-supervision approach that
learns data representations where similar data items are close while distinct data items are far apart.
In \(\mathsf {Starmie}\) , we adopt the SimCLR algorithm [1]}
which was recently shown to be effective in computer vision and NLP applications.
Figure REF  illustrates the high-level idea of the algorithm.
The goal is to learn a column encoder \(\mathcal {M}\)  for tables.
For simplicity, we first assume that \(\mathcal {M}\)  takes a single column as input at a time.
